Index: branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/StudentTProcessRegressionModelCreator.cs
===================================================================
--- branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/StudentTProcessRegressionModelCreator.cs	(revision 13948)
+++ 	(revision )
@@ -1,81 +1,0 @@
-﻿#region License Information
-/* HeuristicLab
- * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
- *
- * This file is part of HeuristicLab.
- *
- * HeuristicLab is free software: you can redistribute it and/or modify
- * it under the terms of the GNU General Public License as published by
- * the Free Software Foundation, either version 3 of the License, or
- * (at your option) any later version.
- *
- * HeuristicLab is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- * GNU General Public License for more details.
- *
- * You should have received a copy of the GNU General Public License
- * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
- */
-#endregion
-
-using System;
-using System.Linq;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Data;
-using HeuristicLab.Encodings.RealVectorEncoding;
-using HeuristicLab.Parameters;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-using HeuristicLab.Problems.DataAnalysis;
-
-namespace HeuristicLab.Algorithms.DataAnalysis {
-  [StorableClass]
-  [Item(Name = "StudentTProcessRegressionModelCreator",
-    Description = "Creates a Student-t process model for regression given the data, the hyperparameters, a mean function, and a covariance function.")]
-  public sealed class StudentTProcessRegressionModelCreator : GaussianProcessModelCreator, IGaussianProcessRegressionModelCreator {
-    private const string ProblemDataParameterName = "ProblemData";
-
-    #region Parameter Properties
-    public ILookupParameter<IRegressionProblemData> ProblemDataParameter {
-      get { return (ILookupParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
-    }
-    #endregion
-
-    #region Properties
-    private IRegressionProblemData ProblemData {
-      get { return ProblemDataParameter.ActualValue; }
-    }
-    #endregion
-    [StorableConstructor]
-    private StudentTProcessRegressionModelCreator(bool deserializing) : base(deserializing) { }
-    private StudentTProcessRegressionModelCreator(StudentTProcessRegressionModelCreator original, Cloner cloner) : base(original, cloner) { }
-    public StudentTProcessRegressionModelCreator()
-      : base() {
-      Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName, "The regression problem data for the Gaussian process model."));
-    }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new StudentTProcessRegressionModelCreator(this, cloner);
-    }
-
-    public override IOperation Apply() {
-      try {
-        var model = Create(ProblemData, Hyperparameter.ToArray(), MeanFunction, CovarianceFunction, ScaleInputValues);
-        ModelParameter.ActualValue = model;
-        NegativeLogLikelihoodParameter.ActualValue = new DoubleValue(model.NegativeLogLikelihood);
-        HyperparameterGradientsParameter.ActualValue = new RealVector(model.HyperparameterGradients);
-        return base.Apply();
-      }
-      catch (ArgumentException) { }
-      catch (alglib.alglibexception) { }
-      NegativeLogLikelihoodParameter.ActualValue = new DoubleValue(1E300);
-      HyperparameterGradientsParameter.ActualValue = new RealVector(Hyperparameter.Count());
-      return base.Apply();
-    }
-
-    public static IGaussianProcessModel Create(IRegressionProblemData problemData, double[] hyperparameter, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction, bool scaleInputs = true) {
-      return new StudentTProcessModel(problemData.Dataset, problemData.TargetVariable, problemData.AllowedInputVariables, problemData.TrainingIndices, hyperparameter, meanFunction, covarianceFunction, scaleInputs);
-    }
-  }
-}
